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Current issue

ELEKTRO 2/2017 was released on February 17th 2017. Its digital version will be available on March 10th 2017.

 

Topic: Electrical appliances – switching, protective and signalling; Devices for smart grids

 

Main Article

Atypical concept of DC power supply source for high current consumption

SVĚTLO (Light) 1/2017 was released on February 7th 2017. Its digital version will be available on March 7th 2017.

Fair and exhibitions
Invitation on LIGHT IN ARCHITECTURE exhibition 

Architectural and scenic lighting
Lighting design in a nutshell
Spiegeltent illumination and its specificity

Getting More Miles From Plug-in Hybrids

17.02.2016 | University of California | ucrtoday.ucr.edu

Plug-in hybrid electric vehicles (PHEVs) can reduce fuel consumption and greenhouse gas emissions compared to their gas-only counterparts. Researchers at the University of California, Riverside’s Bourns College of Engineering have taken the technology one step further, demonstrating how to improve the efficiency of current PHEVs by almost 12 percent.

Since plug-in hybrids combine gas or diesel engines with electric motors and large rechargeable batteries, a key component is an energy management system (EMS) that controls when they switch from ‘all-electric’ mode, during which stored energy from their batteries is used, to ‘hybrid’ mode, which utilizes both fuel and electricity. As new EMS devices are developed, an important consideration is combining the power streams from both sources in the most energy-efficient way.

Better efficiency of hybrid systems

While the UCR EMS does require trip-related information, it also gathers data in real time using onboard sensors and communications devices, rather than demanding it upfront. It is one of the first systems based on a machine learning technique called reinforcement learning (RL).

In comparison-based tests on a 20-mile commute in Southern California, the UCR EMS outperformed currently available binary mode systems, with average fuel savings of 11.9 percent. Even better, the system gets smarter the more it’s used and is not model- or driver-specific, meaning it can be applied to any PHEV driven by any individual.

Read more at University of California

Image Credit: Wikipedia

-jk-